A few years ago, “advanced stats” in basketball meant plus-minus and shot charts. Now, the NBA is wiring the game into the cloud and using AI to surface things fans and coaches could feel but never measure. Through a deep partnership with Amazon Web Services (AWS), the NBA now tracks 29 data points per player in real time—billions of on-court data points across the league. That raw information feeds NBA Inside the Game powered by AWS , a new platform designed to translate com

In this final chapter of my NBA Insights project, I walk through the complete deployment process—turning a local machine learning app into a live web application on Hugging Face Spaces. Learn how I handled Docker vs. Streamlit SDK choices, securely managed secrets like Google API credentials, and worked around model file upload issues using Hugging Face Datasets. This post is packed with real deployment lessons, code snippets, and a look ahead at what’s coming next for the NB

In this three-week sprint, I took NBA Insights from a basic prediction script to a powerful AI system that understands momentum, rest, and game context. From experimenting with Logistic Regression vs. XGBoost to engineering features like win streaks and opponent win rates, this post documents how I trained, evaluated, and deployed an NBA prediction model that hit 70% accuracy on unseen data. Whether you're into machine learning, sports analytics, or building AI tools with rea

In the first two weeks of our NBA Insights project, we collected real NBA data using nba_api, explored trends like home-court advantage, and engineered powerful features using rolling stats and matchup dynamics. This blog covers how we laid the foundation for predicting NBA game outcomes using Python and machine learning.